Surface Defect Detection in Aircraft Skin and Visual Navigation Based on Forced Feature Selection through Segmentation

Abstract

Visual inspection of aircraft skin for surface defects is an area of maintenance that is particularly intensive for time and manpower. One novel way to combat this problem is through the use of computer vision and the advent of Artificial Neural Networks (ANN), or more specifically, semantic segmentation via Convolutional Neural Networks (CNN). The research in the paper explores the use of semantic segmentation of aerial imagery as a way to force feature selection onto key areas of an image that might be more likely to correspond under seasonal variations. Utilizing feature selection and matching on the masked aerial image and the satellite image produces a set of reliable key points that can be used for camera pose estimation and visual navigation.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1132718

Entities

People

  • Tyler B. Hussey

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computational Science
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Data Mining
  • Data Science
  • Dimensionality Reduction
  • Image Processing
  • Image Recognition
  • Information Science
  • Information Systems
  • Machine Learning
  • Network Science
  • Neural Networks
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space